revamp of code for chapter 3

Chapter 3 - Offices and Restrooms/example3-10.rb

@@ -0,0 +1,63 @@
+require 'csv'
+require './restroom'
+
+# Simulation script 3 (and 4)
+
+max_frequency = 5 # how many times a person goes to the restroom with the period
+facilities_per_restroom = 3
+max_use_duration = 1
+population_range = 10..600 # using a population range of 0 to 500 people
+max_num_of_restrooms = 1..4
+
+max_num_of_restrooms.each do |num_of_restrooms|
+  data = {}
+  population_range.step(10).each do |population_size|
+    Person.population.clear
+    population_size.times { Person.population << Person.new(rand(max_frequency)+1, rand(max_use_duration)+1) } # create the population
+    data[population_size] = [] #initialize the temp data store
+    restrooms = []
+    num_of_restrooms.times {restrooms << Restroom.new(facilities_per_restroom)} # create the restroom    
+
+    # iterate over a period
+    DURATION.times do |t|
+      restroom_shortest_queue = restrooms.min {|n,m| n.queue.size <=> m.queue.size }
+      data[population_size] <<  restroom_shortest_queue.queue.size # we want the queue size
+      restrooms.each {|restroom|
+        queue = restroom.queue.clone # clone the queue so that we don't mess up the live one
+        restroom.queue.clear # clear the queue
+
+        # let everyone from the queue enter the restroom first
+        until queue.empty? 
+          restroom.enter queue.shift # de-queue the first person in line and move him to the restroom
+        end
+      }
+      # for each person in the population check if he needs to go
+      Person.population.each do |person|
+        person.frequency = (t > 270 and t < 390) ? 12 : rand(max_frequency)+1 # peak for lunch time
+        if person.need_to_go?
+          restroom = restrooms.min {|a,b| a.queue.size <=> b.queue.size}        
+          restroom.enter person
+        end
+      end
+      restrooms.each {|restroom| restroom.tick }
+    end
+
+  end
+
+  # write the temp store into CSV
+  CSV.open("simulation3-#{num_of_restrooms}.csv", 'w') do |csv|
+    # setup labels
+    lbl = []
+    population_range.step(10).each {|population_size| lbl << population_size  }
+    csv << lbl  
+
+    # write the data
+    DURATION.times do |t|
+      row = []
+      population_range.step(10).each do |population_size|
+        row << data[population_size][t]
+      end
+      csv << row
+    end
+  end
+end

Chapter 3 - Offices and Restrooms/example3-10b.rb

@@ -0,0 +1,56 @@
+require 'csv'
+require './restroom'
+
+# Simulation script 3 (generating data for final simulation)
+
+max_frequency = 5 # how many times a person goes to the restroom with the period
+facilities_per_restroom = 12
+max_use_duration = 1
+population_range = 10..600 # using a population range of 0 to 500 people
+
+data = {}
+population_range.step(10).each do |population_size|
+  Person.population.clear
+  population_size.times { Person.population << Person.new(rand(max_frequency)+1, rand(max_use_duration)+1) } # create the population
+  data[population_size] = [] #initialize the temp data store
+  restroom = Restroom.new facilities_per_restroom # create the restroom    
+
+  # iterate over a period
+  DURATION.times do |t|
+    data[population_size] << restroom.queue.size # we want the queue size
+    queue = restroom.queue.clone # clone the queue so that we don't mess up the live one
+    restroom.queue.clear # clear the queue
+
+    # let everyone from the queue enter the restroom first
+    until queue.empty? 
+      restroom.enter queue.shift # de-queue the first person in line and move him to the restroom
+    end
+
+    # for each person in the population check if he needs to go
+    Person.population.each do |person|
+      person.frequency = (t > 270 and t < 390) ? 12 : rand(max_frequency)+1 # peak for lunch time
+      if person.need_to_go?
+        restroom.enter person
+      end
+    end
+    restroom.tick
+  end
+
+end
+
+# write the temp store into CSV
+CSV.open('simulation3.csv', 'w') do |csv|
+  # setup labels
+  lbl = []
+  population_range.step(10).each {|population_size| lbl << population_size  }
+  csv << lbl  
+
+  # write the data
+  DURATION.times do |t|
+    row = []
+    population_range.step(10).each do |population_size|
+      row << data[population_size][t]
+    end
+    csv << row
+  end
+end

Chapter 3 - Offices and Restrooms/example3-11a.r

@@ -0,0 +1,21 @@
+library(ggplot2)
+
+pdf("figure3-7.pdf")
+
+df <- function(sim) {
+  data <- read.table(paste(sim,".csv",sep=""), header=TRUE, sep=",")
+  max  <- apply(data,2,max)
+  return(data.frame(population=seq(from=10,to=600,by=10),max=max))
+}
+
+ggplot() + scale_shape_manual(name="Type", values=c(2,3,4,22)) + 
+  geom_smooth(data = df("simulation3-1"), aes(x=population,y=max)) +
+  geom_point(data  = df("simulation3-1"), aes(x=population,y=max,shape="max1")) +
+  geom_smooth(data = df("simulation3-2"), aes(x=population,y=max)) +
+  geom_point(data  = df("simulation3-2"), aes(x=population,y=max,shape="max2")) +  
+  geom_smooth(data = df("simulation3-3"), aes(x=population,y=max)) +
+  geom_point(data  = df("simulation3-3"), aes(x=population,y=max,shape="max3")) +
+  geom_smooth(data = df("simulation3-4"), aes(x=population,y=max)) +
+  geom_point(data  = df("simulation3-4"), aes(x=population,y=max,shape="max4")) +    
+  scale_y_continuous("queue size") +
+  scale_x_continuous("population")

Chapter 3 - Offices and Restrooms/example3-11b.r

@@ -0,0 +1,15 @@
+library(ggplot2)
+
+pdf("figure3-8.pdf")
+data <- read.table("simulation3-1.csv", header=TRUE, sep=",")
+df <- data.frame(table(data$X70))
+colnames(df) <- c("queue_size", "frequency")
+percent_labels <- paste(df$frequency, '(', round(df$frequency*100/540, 2), '%)')
+
+ggplot(data=df) + opts(legend.position = "none") + scale_fill_grey(start = 0.5, end = 0.8) + 
+  geom_bar(aes(x = queue_size, y = frequency, fill = factor(queue_size))) +
+  geom_text(aes(x = queue_size, y = frequency, label = percent_labels, size=1)) +
+  scale_y_continuous("frequency") +
+  scale_x_discrete("queue size")
+
+print(df)

Chapter 3 - Offices and Restrooms/example3-12.r

@@ -0,0 +1,17 @@
+library(ggplot2)
+
+pdf("figure3-9.pdf")
+
+df <- function(sim) {
+  data <- read.table(paste(sim,".csv",sep=""), header=TRUE, sep=",")
+  max  <- apply(data,2,max)
+  return(data.frame(population=seq(from=10,to=600,by=10),max=max))
+}
+
+ggplot() + scale_shape_manual(name="Type", values=c(2,3,4,22)) + 
+  geom_smooth(data = df("simulation3"), aes(x=population,y=max)) +
+  geom_point(data  = df("simulation3"), aes(x=population,y=max,shape="max-1x12")) +
+  geom_smooth(data = df("simulation3-4"), aes(x=population,y=max)) +
+  geom_point(data  = df("simulation3-4"), aes(x=population,y=max,shape="max-4x3")) +  
+  scale_y_continuous("queue size") +
+  scale_x_continuous("population")

Chapter 3 - Offices and Restrooms/example3-13.r

@@ -0,0 +1,16 @@
+library(ggplot2)
+
+pdf("figure3-10.pdf")
+
+data <- read.table("simulation3-4.csv", header=TRUE, sep=",")
+df <- data.frame(table(data$X400))
+colnames(df) <- c("queue_size", "frequency")
+percent_labels <- paste(df$frequency, '(', round(df$frequency*100/540, 2), '%)')
+
+ggplot(data=df) + opts(legend.position = "none") + 
+  geom_bar(aes(x = queue_size, y = frequency, fill = factor(queue_size))) +
+  geom_text(aes(x = queue_size, y = frequency, label = percent_labels, size=1)) +
+  scale_y_continuous("frequency") +
+  scale_x_discrete("queue size")
+
+print(df)

Chapter 3 - Offices and Restrooms/example3-4.rb

@@ -0,0 +1,55 @@
+require 'csv'
+require './restroom'
+
+# Simulation script 1 
+
+frequency = 3 # how many times a person goes to the restroom with the period
+facilities_per_restroom = 3
+use_duration = 1
+population_range = 10..600 # using a population range of 0 to 500 people
+
+data = {}
+population_range.step(10).each do |population_size|
+  Person.population.clear
+  population_size.times { Person.population << Person.new(frequency, use_duration) } # create the population
+  data[population_size] = [] #initialize the temp data store
+  restroom = Restroom.new facilities_per_restroom # create the restroom    
+
+  # iterate over a period
+  DURATION.times do |t|
+    data[population_size] << restroom.queue.size # we want the queue size
+    queue = restroom.queue.clone # clone the queue so that we don't mess up the live one
+    restroom.queue.clear # clear the queue
+
+    # let everyone from the queue enter the restroom first
+    until queue.empty? 
+      restroom.enter queue.shift # de-queue the first person in line and move him to the restroom
+    end
+
+
+    # for each person in the population check if he needs to go
+    Person.population.each do |person|
+      if person.need_to_go?
+        restroom.enter person
+      end
+    end
+    restroom.tick
+  end
+end
+
+# write the temp store into CSV
+CSV.open('simulation1.csv', 'w') do |csv|
+  # setup labels
+  lbl = []
+  population_range.step(10).each {|population_size| lbl << population_size  }
+  csv << lbl  
+
+  # write the data
+  DURATION.times do |t|
+    row = []
+    population_range.step(10).each do |population_size|
+      row << data[population_size][t]
+    end
+    csv << row
+  end
+end

Chapter 3 - Offices and Restrooms/example3-5.r

@@ -0,0 +1,19 @@
+library(ggplot2)
+
+pdf("figure3-2.pdf")
+data <- read.table("simulation1.csv", header=TRUE, sep=",")
+mean <- mean(data)
+median <- apply(data,2,median)
+max  <- apply(data,2,max)
+df <- data.frame(population=seq(from=10,to=600,by=10),mean=mean, median=median,max=max)
+
+ggplot(data = df) + scale_shape_manual(name="Type", values=c(2,3,4)) + 
+  geom_smooth(aes(x = population, y = mean)) +
+  geom_point(aes(x  = population, y = mean, shape = "mean")) +
+  geom_smooth(aes(x = population, y = median)) +
+  geom_point(aes(x  = population, y = median, shape = "median")) +  
+  geom_smooth(aes(x = population, y = max)) +
+  geom_point(aes(x  = population, y = max, shape = "max")) +
+  scale_y_continuous("queue size") +
+  scale_x_continuous("population")  
+

Chapter 3 - Offices and Restrooms/example3-6.r

@@ -0,0 +1,13 @@
+library(ggplot2)
+
+pdf("figure3-4.pdf")
+data <- read.table("simulation1.csv", header=TRUE, sep=",")
+df <- data.frame(table(data$X70))
+colnames(df) <- c("queue_size", "frequency")
+percent_labels <- paste(df$frequency, '(', round(df$frequency*100/540, 2), '%)')
+
+ggplot(data=df) + opts(legend.position = "none") + scale_fill_grey(start = 0.6, end = 0.8) + 
+  geom_bar(aes(x = queue_size, y = frequency, fill = factor(queue_size))) +
+  geom_text(aes(x = queue_size, y = frequency, label = percent_labels, size=1)) +
+  scale_y_continuous("frequency") +
+  scale_x_discrete("queue size")

Chapter 3 - Offices and Restrooms/example3-7.rb

@@ -0,0 +1,53 @@
+require 'csv'
+require './restroom'
+
+# Simulation script 2
+
+frequency = 3 # how many times a person goes to the restroom with the period
+use_duration = 1
+population_size = 1000 # using a population range of 1000 people
+facilities_per_restroom_range = 1..30
+data = {}
+facilities_per_restroom_range.each do |facilities_per_restroom|
+  Person.population.clear
+  population_size.times { Person.population << Person.new(frequency, use_duration) } # create the population
+  data[facilities_per_restroom] = [] #initialize the temp data store
+  restroom = Restroom.new facilities_per_restroom # create the restroom    
+
+  # iterate over a period
+  DURATION.times do |t|
+    queue = restroom.queue.clone # clone the queue so that we don't mess up the live one
+    restroom.queue.clear # clear the queue
+    data[facilities_per_restroom] << queue.size # we want the queue size
+
+    # let everyone from the queue enter the restroom first
+    until queue.empty? 
+      restroom.enter queue.shift # de-queue the first person in line and move him to the restroom
+    end
+
+    # for each person in the population check if he needs to go
+    Person.population.each do |person|
+      if person.need_to_go?
+        restroom.enter person
+      end
+    end
+    restroom.tick
+  end
+end
+
+# write the temp store into CSV
+CSV.open('simulation2.csv', 'w') do |csv|
+  # setup labels
+  lbl = []
+  facilities_per_restroom_range.each {|facilities_per_restroom| lbl << facilities_per_restroom  }
+  csv << lbl  
+
+  # write the data
+  DURATION.times do |t|
+    row = []
+    facilities_per_restroom_range.each do |facilities_per_restroom|
+      row << data[facilities_per_restroom][t]
+    end
+    csv << row
+  end
+end

Chapter 3 - Offices and Restrooms/example3-8.r

@@ -0,0 +1,20 @@
+library(ggplot2)
+
+pdf("figure3-5.pdf")
+data <- read.table("simulation2.csv", header = TRUE, sep = ",")
+mean <- mean(data)
+median <- apply(data, 2, median)
+max  <- apply(data, 2, max)
+df <- data.frame(population = seq(from = 1, to = 30), mean = mean, median = median, max = max)
+
+ggplot(data  =  df) + scale_shape_manual(name = "Type", values=c(2,3,4)) + 
+  geom_smooth(aes(x = population, y = mean)) +
+  geom_point(aes(x = population, y = mean, shape = "mean")) +
+  geom_smooth(aes(x = population, y = median)) +
+  geom_point(aes(x = population, y = median, shape = "median")) +  
+  geom_smooth(aes(x = population, y = max)) +
+  geom_point(aes(x = population, y = max, shape = "max")) +
+  scale_y_continuous("queue size") +
+  scale_x_continuous("number of facilities in a restroom")
+
+print(df)

Chapter 3 - Offices and Restrooms/example3-9.r

@@ -0,0 +1,16 @@
+library(ggplot2)
+
+pdf("figure3-6.pdf")
+
+data <- read.table("simulation2.csv", header=TRUE, sep=",")
+df <- data.frame(table(data$X19))
+colnames(df) <- c("queue_size", "frequency")
+percent_labels <- paste(df$frequency, '(', round(df$frequency*100/540, 2), '%)')
+
+ggplot(data=df) + opts(legend.position = "none") + scale_fill_grey(start = 0.6, end = 0.8) +  
+  geom_bar(aes(x = queue_size, y = frequency, fill = factor(queue_size))) +
+  geom_text(aes(x = queue_size, y = frequency, label = percent_labels, size=1)) +
+  scale_y_continuous("frequency") +
+  scale_x_discrete("queue size")
+
+print(df)

Chapter 3 - Offices and Restrooms/restroom.rb

@@ -18,6 +18,7 @@ def enter(person)
       unoccupied_facility.occupy person
     else
       @queue << person
+      Person.population.delete person
     end
   end